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Empowering the healthier citizens of tomorrow

by Dr Kashif Mirza | Peter G. Bresko
Published on April 17, 2023

In recent years, healthcare analytics and AI have become increasingly important, helping healthcare providers make data-driven decisions that can help improve patient-centered outcomes.

By leveraging AI-based large language models (LLMs), there’s an opportunity to complement healthcare analytics by providing additional insights and facilitating more effective data analysis. For example, AI-based models can support healthcare analytics by analyzing unstructured clinical data, such as patient notes or physician comments, which may not be captured by traditional tools. Similarly, with AI-based natural language processing capabilities, healthcare providers can identify important insights from unstructured data and incorporate those into their assessments and planning, leading to better care like never before.

How does it help clinicians?

Additionally, with AI’s ability to process and learn from large amounts of data quickly, this technology has the potential to provide clinicians with evidence-based insights and recommendations at the point of care in minimal time, which can further help providers make more informed decisions about patient-specific care, optimize healthcare operations, and reduce costs. For example, studies relating to a specific treatment could be input into AI and an analysis could be performed to determine the percentage of treatments that were most effective. This is a huge leap forward in how information can be collated and aggregated for easier decision-making.

Another positive development in using AI for healthcare analytics is by improving communication and collaboration among healthcare providers. By providing a common language and framework for analyzing data, technology can help healthcare providers from different disciplines work together more effectively. This can improve care coordination, reduce errors, and ultimately, improve patient outcomes. Using AI to consolidate hand-off reports is a potential benefit for clinicians, especially from physician to physician or nurse to nurse. AI can quickly consolidate documentation for the shift into something easily readable and actioned.

How does AI help organization fulfill the Quintuple Aim?

There is an opportunity for AI to help organizations achieve the Quintuple Aim, which is critical to improving value delivery and creating better outcomes. The Institute for Healthcare Improvement (IHI) has defined the Quintuple Aim as a framework for improving healthcare by addressing the needs of patients, clinicians, and the healthcare system. LLM can help realize the Quintuple Aim by providing insights that improve patient outcomes, reduce costs, and enhance the experience of both patients and clinicians.

  • When considering clinician experience, AI has the potential to provide consolidated views of notes, which can help save clinicians’ time spent in the electronic health record.
  • To improve health outcomes, AI can assist in collating large amounts of data from studies to provide recommendations for a specific disease treatment plan.
  • To enhance financial outcomes, AI can help find efficiencies via its ability to aggregate large amounts of data to identify where spending can be impacted in a succinct way that’s easy to understand and action.
  • For improved patient experience, data from surveys like Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) can be input and AI can distill that information into actionable insights.
  • Lastly, health equity, the fifth pillar of the Quintuple Aim, can be addressed using AI to determine, for example, non-communicable disease rates in specific geographies.

In essence, critical information can be gleaned from AI to improve upon the five pillars of the Quintuple Aim. 

In conclusion, these new AI features and technologies have the potential to advance healthcare analytics by providing additional insights, facilitating more effective data analysis, providing real-time insights and recommendations, improving communication and collaboration among healthcare providers, and improving patient engagement and satisfaction. By using this technology to complement existing efforts, the opportunity to improve healthcare is on the horizon.